PCA is a method for reducing the 100 variables (wavelength data) in each spectrum down to just a few important variables. These variables are often referred to as latent variables, principal components, factors, eigenvectors, etc, and are vectors. This manual will refer to them as PC’s. The dot product of these vectors with the spectral data yields scalars called “PC scores”. Unknowns can be identified by comparing the PC scores of unknown materials to those of the model.
比如这个方法，也叫主成分分析法（PCA）作者: iop 时间: 2015-12-12 20:57
近红外定性分析方法有STMD（Sample To Model Distance）、马氏距离、欧氏距离等，目前我感觉用欧氏距离做定性效果是最好的，但是有时也要具体问题具体分析，也可以几种方法联用，要看具体定性效果来定。作者: shuishui 时间: 2015-12-12 20:58
